Cost‐Utility Analysis of Low‐Dose Pioglitazone in a Population With Prediabetes and a History of Stroke or Transient Ischemic Attack
Bibliographic record
Abstract
BACKGROUND: Pioglitazone significantly reduces the risk of stroke in people with diabetes, and in those with prediabetes, it markedly reduces the risk of stroke/myocardial infarction and new-onset diabetes. Low-dose pioglitazone provides most of the clinical benefits of high-dose pioglitazone, with fewer adverse effects. We report an economic evaluation of the cost-effectiveness of low-dose pioglitazone versus placebo from a Canadian public payer perspective in 2023 Canadian dollars. METHODS AND RESULTS: A Markov model was developed at a lifetime horizon with an annual cycle length and 5 health states (event-free, myocardial infarction, stroke, new-onset diabetes, and death). Transition probabilities were extracted from the IRIS (Insulin Resistance Intervention in Stroke) trial. Health state costs and utilities were based on public sources. Annual discount rates of 1.5% were applied in the reference-case analysis. Probabilistic analyses were conducted to deal with parameter uncertainty through 5000 simulations. The costs were estimated as $24 887 (interquartile range [IQR], $14 632-$41507) for low-dose pioglitazone and $57 301 (IQR, $48 730-$67368) for placebo, resulting in a cost saving of -$30 287 (IQR, -$43 374 to -$14 587) in favor of low-dose pioglitazone. Quality-adjusted life years were estimated as 25.99 (IQR, 24.56-26.81) for the low-dose pioglitazone and 19.44 (IQR, 18.68-20.13) for placebo, resulting in a difference of 6.37 (IQR, 5.07-7.36) in favor of low-dose pioglitazone. Consistent findings were observed from scenario analyses and 1-way probability sensitivity analyses. CONCLUSIONS: Holding across a wide range of values in modeling parameters, low-dose pioglitazone is found as the dominant strategy versus a placebo.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".